Overview

Brought to you by YData

Dataset statistics

Number of variables61
Number of observations74612
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)< 0.1%
Total size in memory10.3 MiB
Average record size in memory145.0 B

Variable types

Numeric7
Categorical4
DateTime1
Boolean49

Alerts

Dataset has 8 (< 0.1%) duplicate rowsDuplicates
Classification_Of_Accident_Non-fatal injury is highly overall correlated with Classification_Of_Accident_P.D. only and 2 other fieldsHigh correlation
Classification_Of_Accident_P.D. only is highly overall correlated with Classification_Of_Accident_Non-fatal injury and 2 other fieldsHigh correlation
Environment_Condition_Rain is highly overall correlated with Road_Surface_Condition_WetHigh correlation
Environment_Condition_Snow is highly overall correlated with Road_Surface_Condition_Loose snowHigh correlation
Initial_Impact_Type_SMV unattended vehicle is highly overall correlated with Light_UnknownHigh correlation
Lat is highly overall correlated with LongHigh correlation
Light_Unknown is highly overall correlated with Initial_Impact_Type_SMV unattended vehicleHigh correlation
Location_Type_Midblock is highly overall correlated with Traffic_Control_No control and 1 other fieldsHigh correlation
Long is highly overall correlated with LatHigh correlation
Max_Injury_Fatal is highly overall correlated with Num_of_Fatal_InjuriesHigh correlation
Max_Injury_Minimal is highly overall correlated with Classification_Of_Accident_Non-fatal injury and 1 other fieldsHigh correlation
Max_Injury_Minor is highly overall correlated with Classification_Of_Accident_Non-fatal injury and 1 other fieldsHigh correlation
Num_of_Fatal_Injuries is highly overall correlated with Max_Injury_Fatal and 4 other fieldsHigh correlation
Num_of_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 2 other fieldsHigh correlation
Num_of_Major_Injuries is highly overall correlated with Num_of_Fatal_InjuriesHigh correlation
Num_of_Minimal_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 1 other fieldsHigh correlation
Num_of_Minor_Injuries is highly overall correlated with Num_of_Fatal_Injuries and 1 other fieldsHigh correlation
Road_Surface_Condition_Loose snow is highly overall correlated with Environment_Condition_SnowHigh correlation
Road_Surface_Condition_Wet is highly overall correlated with Environment_Condition_RainHigh correlation
Traffic_Control_No control is highly overall correlated with Location_Type_Midblock and 1 other fieldsHigh correlation
Traffic_Control_Traffic signal is highly overall correlated with Location_Type_Midblock and 1 other fieldsHigh correlation
Num_Of_Pedestrians is highly imbalanced (92.3%) Imbalance
Num_of_Bicycles is highly imbalanced (93.4%) Imbalance
Num_of_Motorcycles is highly imbalanced (96.4%) Imbalance
Num_of_Fatal_Injuries is highly imbalanced (99.0%) Imbalance
Initial_Impact_Type_Approaching is highly imbalanced (88.8%) Imbalance
Initial_Impact_Type_Other is highly imbalanced (83.6%) Imbalance
Initial_Impact_Type_SMV unattended vehicle is highly imbalanced (58.5%) Imbalance
Initial_Impact_Type_Turning movement is highly imbalanced (51.9%) Imbalance
Road_Surface_Condition_Ice is highly imbalanced (75.5%) Imbalance
Road_Surface_Condition_Loose sand or gravel is highly imbalanced (99.0%) Imbalance
Road_Surface_Condition_Loose snow is highly imbalanced (66.8%) Imbalance
Road_Surface_Condition_Mud is highly imbalanced (99.8%) Imbalance
Road_Surface_Condition_Other is highly imbalanced (99.4%) Imbalance
Road_Surface_Condition_Packed snow is highly imbalanced (81.9%) Imbalance
Road_Surface_Condition_Slush is highly imbalanced (78.1%) Imbalance
Road_Surface_Condition_Spilled liquid is highly imbalanced (99.9%) Imbalance
Road_Surface_Condition_Unknown is highly imbalanced (99.4%) Imbalance
Environment_Condition_Drifting Snow is highly imbalanced (96.3%) Imbalance
Environment_Condition_Fog, mist, smoke, dust is highly imbalanced (97.5%) Imbalance
Environment_Condition_Freezing Rain is highly imbalanced (88.8%) Imbalance
Environment_Condition_Other is highly imbalanced (99.5%) Imbalance
Environment_Condition_Rain is highly imbalanced (57.0%) Imbalance
Environment_Condition_Snow is highly imbalanced (51.9%) Imbalance
Environment_Condition_Strong wind is highly imbalanced (98.5%) Imbalance
Environment_Condition_Unknown is highly imbalanced (98.3%) Imbalance
Light_Dawn is highly imbalanced (83.6%) Imbalance
Light_Dusk is highly imbalanced (73.0%) Imbalance
Light_Other is highly imbalanced (99.8%) Imbalance
Light_Unknown is highly imbalanced (77.6%) Imbalance
Traffic_Control_MPS is highly imbalanced (99.4%) Imbalance
Traffic_Control_Other is highly imbalanced (99.2%) Imbalance
Traffic_Control_Ped. crossover is highly imbalanced (99.6%) Imbalance
Traffic_Control_Roundabout is highly imbalanced (89.8%) Imbalance
Traffic_Control_School bus is highly imbalanced (99.9%) Imbalance
Traffic_Control_Stop sign is highly imbalanced (50.2%) Imbalance
Traffic_Control_Traffic controller is highly imbalanced (99.8%) Imbalance
Traffic_Control_Traffic gate is highly imbalanced (99.8%) Imbalance
Traffic_Control_Yield sign is highly imbalanced (94.9%) Imbalance
Max_Injury_Fatal is highly imbalanced (98.0%) Imbalance
Max_Injury_Major is highly imbalanced (92.8%) Imbalance
Max_Injury_Minimal is highly imbalanced (64.5%) Imbalance
Max_Injury_Minor is highly imbalanced (52.4%) Imbalance
Max_Injury_None is highly imbalanced (99.2%) Imbalance
Num_of_Major_Injuries is highly skewed (γ1 = 31.27932899) Skewed
Lat is highly skewed (γ1 = -23.8028575) Skewed
Num_of_Injuries has 61195 (82.0%) zeros Zeros
Num_of_Minimal_Injuries has 68879 (92.3%) zeros Zeros
Num_of_Minor_Injuries has 66808 (89.5%) zeros Zeros
Num_of_Major_Injuries has 73941 (99.1%) zeros Zeros

Reproduction

Analysis started2024-11-21 00:06:44.874088
Analysis finished2024-11-21 00:07:32.736877
Duration47.86 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Num_of_Vehicle
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8412186
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:33.042477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum25
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58651207
Coefficient of variation (CV)0.3185456
Kurtosis36.938837
Mean1.8412186
Median Absolute Deviation (MAD)0
Skewness1.5433167
Sum137377
Variance0.34399641
MonotonicityNot monotonic
2024-11-21T00:07:33.412686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 51248
68.7%
1 18127
 
24.3%
3 4425
 
5.9%
4 660
 
0.9%
5 112
 
0.2%
6 24
 
< 0.1%
7 12
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
1 18127
 
24.3%
2 51248
68.7%
3 4425
 
5.9%
4 660
 
0.9%
5 112
 
0.2%
6 24
 
< 0.1%
7 12
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 12
 
< 0.1%
6 24
 
< 0.1%
5 112
 
0.2%
4 660
 
0.9%
3 4425
 
5.9%
2 51248
68.7%
1 18127
 
24.3%

Num_Of_Pedestrians
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0
73015 
1
 
1535
2
 
58
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74612
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Length

2024-11-21T00:07:33.691071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T00:07:33.989214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73015
97.9%
1 1535
 
2.1%
2 58
 
0.1%
3 4
 
< 0.1%

Num_of_Bicycles
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
73265 
1.0
 
1335
2.0
 
10
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 73265
98.2%
1.0 1335
 
1.8%
2.0 10
 
< 0.1%
3.0 2
 
< 0.1%

Length

2024-11-21T00:07:34.270296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T00:07:34.521561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 73265
98.2%
1.0 1335
 
1.8%
2.0 10
 
< 0.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 147877
66.1%
. 74612
33.3%
1 1335
 
0.6%
2 10
 
< 0.1%
3 2
 
< 0.1%

Num_of_Motorcycles
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
73975 
1.0
 
628
2.0
 
8
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 73975
99.1%
1.0 628
 
0.8%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Length

2024-11-21T00:07:34.826325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T00:07:35.093385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 73975
99.1%
1.0 628
 
0.8%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 148587
66.4%
. 74612
33.3%
1 628
 
0.3%
2 8
 
< 0.1%
3 1
 
< 0.1%

Num_of_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23347451
Minimum0
Maximum38
Zeros61195
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:35.361820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58800648
Coefficient of variation (CV)2.518504
Kurtosis242.63414
Mean0.23347451
Median Absolute Deviation (MAD)0
Skewness6.6191692
Sum17420
Variance0.34575162
MonotonicityNot monotonic
2024-11-21T00:07:35.656053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 61195
82.0%
1 10497
 
14.1%
2 2193
 
2.9%
3 523
 
0.7%
4 129
 
0.2%
5 46
 
0.1%
6 18
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
0 61195
82.0%
1 10497
 
14.1%
2 2193
 
2.9%
3 523
 
0.7%
4 129
 
0.2%
5 46
 
0.1%
6 18
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 5
 
< 0.1%
6 18
 
< 0.1%
5 46
 
0.1%
4 129
 
0.2%
3 523
 
0.7%
2 2193
 
2.9%
1 10497
14.1%

Num_of_Minimal_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093202166
Minimum0
Maximum11
Zeros68879
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:35.939740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35866256
Coefficient of variation (CV)3.8482214
Kurtosis49.645612
Mean0.093202166
Median Absolute Deviation (MAD)0
Skewness5.3905638
Sum6954
Variance0.12863884
MonotonicityNot monotonic
2024-11-21T00:07:36.202846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 68879
92.3%
1 4779
 
6.4%
2 769
 
1.0%
3 138
 
0.2%
4 30
 
< 0.1%
5 10
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 10
 
< 0.1%
4 30
 
< 0.1%
3 138
 
0.2%
2 769
 
1.0%
1 4779
 
6.4%
0 68879
92.3%

Num_of_Minor_Injuries
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12827695
Minimum0
Maximum10
Zeros66808
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:36.444800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42041275
Coefficient of variation (CV)3.2773833
Kurtosis32.912029
Mean0.12827695
Median Absolute Deviation (MAD)0
Skewness4.5402053
Sum9571
Variance0.17674688
MonotonicityNot monotonic
2024-11-21T00:07:36.717727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 66808
89.5%
1 6465
 
8.7%
2 1045
 
1.4%
3 213
 
0.3%
4 48
 
0.1%
5 23
 
< 0.1%
6 6
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 66808
89.5%
1 6465
 
8.7%
2 1045
 
1.4%
3 213
 
0.3%
4 48
 
0.1%
5 23
 
< 0.1%
6 6
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 6
 
< 0.1%
5 23
 
< 0.1%
4 48
 
0.1%
3 213
 
0.3%
2 1045
 
1.4%
1 6465
8.7%

Num_of_Major_Injuries
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0099715863
Minimum0
Maximum14
Zeros73941
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:36.985765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11967469
Coefficient of variation (CV)12.00157
Kurtosis2660.2527
Mean0.0099715863
Median Absolute Deviation (MAD)0
Skewness31.279329
Sum744
Variance0.014322032
MonotonicityNot monotonic
2024-11-21T00:07:37.238413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 73941
99.1%
1 622
 
0.8%
2 39
 
0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 73941
99.1%
1 622
 
0.8%
2 39
 
0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
4 3
 
< 0.1%
3 6
 
< 0.1%
2 39
 
0.1%
1 622
 
0.8%
0 73941
99.1%

Num_of_Fatal_Injuries
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
0.0
74471 
1.0
 
132
2.0
 
8
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters223836
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 74471
99.8%
1.0 132
 
0.2%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Length

2024-11-21T00:07:37.522638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T00:07:37.776779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 74471
99.8%
1.0 132
 
0.2%
2.0 8
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149083
66.6%
. 74612
33.3%
1 132
 
0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Lat
Real number (ℝ)

High correlation  Skewed 

Distinct42389
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.29193
Minimum0
Maximum45.524921
Zeros76
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size583.0 KiB
2024-11-21T00:07:38.052747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.253606
Q145.333451
median45.378984
Q345.418314
95-th percentile45.460853
Maximum45.524921
Range45.524921
Interquartile range (IQR)0.08486305

Descriptive statistics

Standard deviation1.843572
Coefficient of variation (CV)0.040704204
Kurtosis566.14395
Mean45.29193
Median Absolute Deviation (MAD)0.041780475
Skewness-23.802858
Sum3379321.5
Variance3.3987576
MonotonicityNot monotonic
2024-11-21T00:07:38.391051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.46414526 229
 
0.3%
45.33460787 208
 
0.3%
45.48898157 177
 
0.2%
45.46085266 158
 
0.2%
45.33417384 154
 
0.2%
45.35150226 140
 
0.2%
45.32869813 139
 
0.2%
45.27053061 136
 
0.2%
45.37040769 136
 
0.2%
45.41648492 132
 
0.2%
Other values (42379) 73003
97.8%
ValueCountFrequency (%)
0 76
0.1%
3.318485223 50
0.1%
3.341503337 5
 
< 0.1%
44.96795959 1
 
< 0.1%
44.96797999 1
 
< 0.1%
44.96979868 1
 
< 0.1%
44.97028019 1
 
< 0.1%
44.97358922 1
 
< 0.1%
44.98083128 1
 
< 0.1%
44.98106813 1
 
< 0.1%
ValueCountFrequency (%)
45.52492133 1
 
< 0.1%
45.52476088 1
 
< 0.1%
45.52470204 5
< 0.1%
45.52468914 1
 
< 0.1%
45.52464234 1
 
< 0.1%
45.52452523 1
 
< 0.1%
45.52440728 1
 
< 0.1%
45.52440692 1
 
< 0.1%
45.52440179 1
 
< 0.1%
45.52440045 1
 
< 0.1%

Long
Real number (ℝ)

High correlation 

Distinct42172
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-75.710325
Minimum-79.23729
Maximum-75.261583
Zeros0
Zeros (%)0.0%
Negative74612
Negative (%)100.0%
Memory size583.0 KiB
2024-11-21T00:07:38.716014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-79.23729
5-th percentile-75.929355
Q1-75.755176
median-75.696671
Q3-75.642484
95-th percentile-75.498398
Maximum-75.261583
Range3.975707
Interquartile range (IQR)0.11269275

Descriptive statistics

Standard deviation0.16799797
Coefficient of variation (CV)-0.0022189572
Kurtosis196.44678
Mean-75.710325
Median Absolute Deviation (MAD)0.056860655
Skewness-9.6435503
Sum-5648898.8
Variance0.028223318
MonotonicityNot monotonic
2024-11-21T00:07:39.072650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-75.54012559 229
 
0.3%
-75.69319006 208
 
0.3%
-75.47774271 177
 
0.2%
-75.48780963 158
 
0.2%
-75.70057066 154
 
0.2%
-75.76297753 140
 
0.2%
-75.74897241 139
 
0.2%
-75.74638832 136
 
0.2%
-75.66330343 136
 
0.2%
-75.60244499 133
 
0.2%
Other values (42162) 73002
97.8%
ValueCountFrequency (%)
-79.23728992 76
0.1%
-76.33938433 1
 
< 0.1%
-76.33804351 1
 
< 0.1%
-76.3378556 1
 
< 0.1%
-76.33587105 1
 
< 0.1%
-76.33458758 1
 
< 0.1%
-76.33374987 2
 
< 0.1%
-76.33372086 1
 
< 0.1%
-76.3336814 1
 
< 0.1%
-76.33313338 1
 
< 0.1%
ValueCountFrequency (%)
-75.26158294 1
< 0.1%
-75.26813064 1
< 0.1%
-75.26816498 1
< 0.1%
-75.26880567 2
< 0.1%
-75.26893514 1
< 0.1%
-75.27313672 1
< 0.1%
-75.27461775 1
< 0.1%
-75.27512905 1
< 0.1%
-75.27646836 1
< 0.1%
-75.27867728 1
< 0.1%
Distinct70162
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size583.0 KiB
Minimum2017-01-01 00:15:00
Maximum2022-12-30 14:20:00
2024-11-21T00:07:39.711756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:40.085940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location_Type_Midblock
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
40441 
True
34171 
ValueCountFrequency (%)
False 40441
54.2%
True 34171
45.8%
2024-11-21T00:07:40.394276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
61336 
True
13276 
ValueCountFrequency (%)
False 61336
82.2%
True 13276
 
17.8%
2024-11-21T00:07:40.619283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Classification_Of_Accident_P.D. only
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
True
61195 
False
13417 
ValueCountFrequency (%)
True 61195
82.0%
False 13417
 
18.0%
2024-11-21T00:07:40.849744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
73500 
True
 
1112
ValueCountFrequency (%)
False 73500
98.5%
True 1112
 
1.5%
2024-11-21T00:07:41.096685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Initial_Impact_Type_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
72814 
True
 
1798
ValueCountFrequency (%)
False 72814
97.6%
True 1798
 
2.4%
2024-11-21T00:07:41.334677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
49831 
True
24781 
ValueCountFrequency (%)
False 49831
66.8%
True 24781
33.2%
2024-11-21T00:07:41.566662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
62727 
True
11885 
ValueCountFrequency (%)
False 62727
84.1%
True 11885
 
15.9%
2024-11-21T00:07:41.792679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Initial_Impact_Type_SMV unattended vehicle
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
68372 
True
 
6240
ValueCountFrequency (%)
False 68372
91.6%
True 6240
 
8.4%
2024-11-21T00:07:42.013551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
64039 
True
10573 
ValueCountFrequency (%)
False 64039
85.8%
True 10573
 
14.2%
2024-11-21T00:07:42.259206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
66858 
True
7754 
ValueCountFrequency (%)
False 66858
89.6%
True 7754
 
10.4%
2024-11-21T00:07:42.475996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Ice
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
71581 
True
 
3031
ValueCountFrequency (%)
False 71581
95.9%
True 3031
 
4.1%
2024-11-21T00:07:42.692155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74549 
True
 
63
ValueCountFrequency (%)
False 74549
99.9%
True 63
 
0.1%
2024-11-21T00:07:42.904258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Loose snow
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
70053 
True
 
4559
ValueCountFrequency (%)
False 70053
93.9%
True 4559
 
6.1%
2024-11-21T00:07:43.149984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Mud
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74602 
True
 
10
ValueCountFrequency (%)
False 74602
> 99.9%
True 10
 
< 0.1%
2024-11-21T00:07:43.477712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74576 
True
 
36
ValueCountFrequency (%)
False 74576
> 99.9%
True 36
 
< 0.1%
2024-11-21T00:07:43.863153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
72573 
True
 
2039
ValueCountFrequency (%)
False 72573
97.3%
True 2039
 
2.7%
2024-11-21T00:07:44.262297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Slush
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
72004 
True
 
2608
ValueCountFrequency (%)
False 72004
96.5%
True 2608
 
3.5%
2024-11-21T00:07:44.589077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74605 
True
 
7
ValueCountFrequency (%)
False 74605
> 99.9%
True 7
 
< 0.1%
2024-11-21T00:07:45.006779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74574 
True
 
38
ValueCountFrequency (%)
False 74574
99.9%
True 38
 
0.1%
2024-11-21T00:07:45.309995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Road_Surface_Condition_Wet
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
61901 
True
12711 
ValueCountFrequency (%)
False 61901
83.0%
True 12711
 
17.0%
2024-11-21T00:07:45.643716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74322 
True
 
290
ValueCountFrequency (%)
False 74322
99.6%
True 290
 
0.4%
2024-11-21T00:07:45.995414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74425 
True
 
187
ValueCountFrequency (%)
False 74425
99.7%
True 187
 
0.3%
2024-11-21T00:07:46.338101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
73500 
True
 
1112
ValueCountFrequency (%)
False 73500
98.5%
True 1112
 
1.5%
2024-11-21T00:07:46.654154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Environment_Condition_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74583 
True
 
29
ValueCountFrequency (%)
False 74583
> 99.9%
True 29
 
< 0.1%
2024-11-21T00:07:47.053888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Environment_Condition_Rain
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
68050 
True
 
6562
ValueCountFrequency (%)
False 68050
91.2%
True 6562
 
8.8%
2024-11-21T00:07:47.313072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Environment_Condition_Snow
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
66873 
True
7739 
ValueCountFrequency (%)
False 66873
89.6%
True 7739
 
10.4%
2024-11-21T00:07:47.539915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74511 
True
 
101
ValueCountFrequency (%)
False 74511
99.9%
True 101
 
0.1%
2024-11-21T00:07:47.765380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74493 
True
 
119
ValueCountFrequency (%)
False 74493
99.8%
True 119
 
0.2%
2024-11-21T00:07:47.982652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Light_Dawn
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
72809 
True
 
1803
ValueCountFrequency (%)
False 72809
97.6%
True 1803
 
2.4%
2024-11-21T00:07:48.194995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
True
49858 
False
24754 
ValueCountFrequency (%)
True 49858
66.8%
False 24754
33.2%
2024-11-21T00:07:48.426233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Light_Dusk
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
71167 
True
 
3445
ValueCountFrequency (%)
False 71167
95.4%
True 3445
 
4.6%
2024-11-21T00:07:48.630232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Light_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74602 
True
 
10
ValueCountFrequency (%)
False 74602
> 99.9%
True 10
 
< 0.1%
2024-11-21T00:07:48.839898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Light_Unknown
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
71921 
True
 
2691
ValueCountFrequency (%)
False 71921
96.4%
True 2691
 
3.6%
2024-11-21T00:07:49.055205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_MPS
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74579 
True
 
33
ValueCountFrequency (%)
False 74579
> 99.9%
True 33
 
< 0.1%
2024-11-21T00:07:49.260778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_No control
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
39973 
True
34639 
ValueCountFrequency (%)
False 39973
53.6%
True 34639
46.4%
2024-11-21T00:07:49.479175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74564 
True
 
48
ValueCountFrequency (%)
False 74564
99.9%
True 48
 
0.1%
2024-11-21T00:07:49.687178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74588 
True
 
24
ValueCountFrequency (%)
False 74588
> 99.9%
True 24
 
< 0.1%
2024-11-21T00:07:49.897227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Roundabout
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
73620 
True
 
992
ValueCountFrequency (%)
False 73620
98.7%
True 992
 
1.3%
2024-11-21T00:07:50.118918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_School bus
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74607 
True
 
5
ValueCountFrequency (%)
False 74607
> 99.9%
True 5
 
< 0.1%
2024-11-21T00:07:50.327764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Stop sign
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
66447 
True
8165 
ValueCountFrequency (%)
False 66447
89.1%
True 8165
 
10.9%
2024-11-21T00:07:50.551921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74603 
True
 
9
ValueCountFrequency (%)
False 74603
> 99.9%
True 9
 
< 0.1%
2024-11-21T00:07:50.771468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Traffic gate
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74604 
True
 
8
ValueCountFrequency (%)
False 74604
> 99.9%
True 8
 
< 0.1%
2024-11-21T00:07:51.342026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Traffic signal
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
44440 
True
30172 
ValueCountFrequency (%)
False 44440
59.6%
True 30172
40.4%
2024-11-21T00:07:51.560332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Traffic_Control_Yield sign
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74183 
True
 
429
ValueCountFrequency (%)
False 74183
99.4%
True 429
 
0.6%
2024-11-21T00:07:51.767774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Max_Injury_Fatal
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74471 
True
 
141
ValueCountFrequency (%)
False 74471
99.8%
True 141
 
0.2%
2024-11-21T00:07:51.966690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Max_Injury_Major
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
73961 
True
 
651
ValueCountFrequency (%)
False 73961
99.1%
True 651
 
0.9%
2024-11-21T00:07:52.166940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Max_Injury_Minimal
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
69605 
True
 
5007
ValueCountFrequency (%)
False 69605
93.3%
True 5007
 
6.7%
2024-11-21T00:07:52.369923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Max_Injury_Minor
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
66994 
True
7618 
ValueCountFrequency (%)
False 66994
89.8%
True 7618
 
10.2%
2024-11-21T00:07:52.598626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Max_Injury_None
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
False
74560 
True
 
52
ValueCountFrequency (%)
False 74560
99.9%
True 52
 
0.1%
2024-11-21T00:07:52.802898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Interactions

2024-11-21T00:07:26.486984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:11.909030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:13.685157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:15.736917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:18.217022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:20.711271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:24.059736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:26.725507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:12.169784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:13.932680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:16.109872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:19.004627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:20.966404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:24.452291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:27.032838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:12.424654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:14.195327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:16.456160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:19.433865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:21.403068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:24.825323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:27.270502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:12.682326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:14.453459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:16.767485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:19.679613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:21.705277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:25.248301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:27.541331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:12.922146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:14.717083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:17.109482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:19.925021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:22.015602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:25.633319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:27.813913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:13.195940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:15.006614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:17.493172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:20.213874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:22.872068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:25.990850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:28.105962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:13.402925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:15.313881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:17.800779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:20.456523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:23.177963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T00:07:26.229115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-21T00:07:53.107718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Classification_Of_Accident_Non-fatal injuryClassification_Of_Accident_P.D. onlyEnvironment_Condition_Drifting SnowEnvironment_Condition_Fog, mist, smoke, dustEnvironment_Condition_Freezing RainEnvironment_Condition_OtherEnvironment_Condition_RainEnvironment_Condition_SnowEnvironment_Condition_Strong windEnvironment_Condition_UnknownInitial_Impact_Type_ApproachingInitial_Impact_Type_OtherInitial_Impact_Type_Rear endInitial_Impact_Type_SMV otherInitial_Impact_Type_SMV unattended vehicleInitial_Impact_Type_SideswipeInitial_Impact_Type_Turning movementLatLight_DawnLight_DaylightLight_DuskLight_OtherLight_UnknownLocation_Type_MidblockLongMax_Injury_FatalMax_Injury_MajorMax_Injury_MinimalMax_Injury_MinorMax_Injury_NoneNum_Of_PedestriansNum_of_BicyclesNum_of_Fatal_InjuriesNum_of_InjuriesNum_of_Major_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_MotorcyclesNum_of_VehicleRoad_Surface_Condition_IceRoad_Surface_Condition_Loose sand or gravelRoad_Surface_Condition_Loose snowRoad_Surface_Condition_MudRoad_Surface_Condition_OtherRoad_Surface_Condition_Packed snowRoad_Surface_Condition_SlushRoad_Surface_Condition_Spilled liquidRoad_Surface_Condition_UnknownRoad_Surface_Condition_WetTraffic_Control_MPSTraffic_Control_No controlTraffic_Control_OtherTraffic_Control_Ped. crossoverTraffic_Control_RoundaboutTraffic_Control_School busTraffic_Control_Stop signTraffic_Control_Traffic controllerTraffic_Control_Traffic gateTraffic_Control_Traffic signalTraffic_Control_Yield sign
Classification_Of_Accident_Non-fatal injury1.0000.9940.0110.0020.0150.0000.0010.0470.0000.0080.0300.0320.0020.0960.1230.1190.0890.0040.0040.0300.0000.0010.0890.0840.0310.0190.2010.5760.7250.0110.2940.2380.0190.1070.0490.2440.2880.1310.0410.0220.0070.0360.0000.0000.0330.0330.0070.0000.0110.0000.0860.0020.0140.0210.0000.0440.0050.0000.0630.000
Classification_Of_Accident_P.D. only0.9941.0000.0110.0040.0150.0000.0000.0470.0000.0080.0340.0320.0060.1000.1240.1210.0890.0050.0030.0290.0000.0010.0900.0830.0310.0920.2000.5730.7200.0110.3010.2380.0930.1120.0540.2430.2880.1380.0400.0220.0080.0360.0000.0000.0330.0330.0070.0000.0110.0000.0840.0010.0130.0220.0000.0460.0050.0000.0610.000
Environment_Condition_Drifting Snow0.0110.0111.0000.0000.0060.0000.0190.0210.0000.0000.0290.0000.0120.0480.0100.0100.0110.0000.0080.0210.0000.0000.0100.0220.0200.0000.0000.0000.0150.0000.0020.0060.0000.0000.0000.0000.0000.0000.0070.0620.0000.0970.0000.0000.0530.0130.0000.0000.0170.0000.0230.0000.0000.0000.0000.0020.0000.0000.0190.000
Environment_Condition_Fog, mist, smoke, dust0.0020.0040.0001.0000.0030.0000.0150.0160.0000.0000.0000.0040.0100.0440.0070.0070.0100.0000.0290.0370.0000.0000.0070.0140.0300.0060.0070.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0030.0000.0020.0000.0000.0390.0000.0140.0000.0000.0000.0000.0100.0000.0000.0190.000
Environment_Condition_Freezing Rain0.0150.0150.0060.0031.0000.0000.0380.0420.0000.0000.0290.0000.0210.0690.0000.0160.0220.0000.0110.0470.0020.0000.0090.0260.0230.0000.0050.0050.0120.0000.0080.0150.0000.0000.0030.0040.0000.0080.0010.3750.0000.0090.0000.0000.0000.0850.0000.0000.0140.0000.0260.0000.0000.0000.0000.0000.0000.0000.0230.000
Environment_Condition_Other0.0000.0000.0000.0000.0001.0000.0030.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0010.0880.0000.0080.0000.0060.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0080.000
Environment_Condition_Rain0.0010.0000.0190.0150.0380.0031.0000.1050.0100.0110.0090.0140.0000.0270.0310.0000.0000.0000.0060.0290.0030.0000.0170.0180.0000.0050.0040.0000.0020.0000.0180.0130.0040.0000.0000.0000.0060.0220.0060.0460.0050.0710.0000.0060.0490.0340.0000.0010.6550.0040.0190.0000.0000.0030.0000.0040.0000.0000.0170.000
Environment_Condition_Snow0.0470.0470.0210.0160.0420.0040.1051.0000.0110.0130.0590.0000.0200.0780.0000.0360.0300.0000.0470.1170.0220.0000.0040.0180.0110.0080.0130.0190.0380.0050.0230.0430.0080.0090.0000.0100.0150.0300.0150.0990.0000.5540.0000.0000.2190.2700.0000.0060.0310.0020.0180.0070.0000.0090.0000.0000.0000.0000.0170.000
Environment_Condition_Strong wind0.0000.0000.0000.0000.0000.0000.0100.0111.0000.0000.0000.0000.0070.0330.0000.0070.0080.0000.0000.0130.0000.0000.0050.0110.0140.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0170.0000.0000.0360.0000.0000.0000.0000.0100.0000.0000.0000.0070.0000.0100.0000.0000.0000.0000.0020.0000.0000.0050.000
Environment_Condition_Unknown0.0080.0080.0000.0000.0000.0000.0110.0130.0001.0000.0000.0000.0120.0000.0510.0040.0080.0000.0040.0250.0030.0000.0900.0230.0080.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0110.0000.0060.0000.0980.0000.0000.0170.3040.0000.0000.0220.0000.0000.0000.0000.0010.0000.0000.0190.000
Initial_Impact_Type_Approaching0.0300.0340.0290.0000.0290.0000.0090.0590.0000.0001.0000.0190.0870.0530.0370.0500.0420.0000.0170.0130.0000.0000.0230.0830.0270.0290.0330.0030.0320.0000.0170.0000.0330.0250.0540.0120.0540.0000.0000.0470.0000.0500.0000.0000.0380.0240.0000.0000.0110.0000.0820.0000.0000.0060.0000.0090.0000.0000.0740.006
Initial_Impact_Type_Other0.0320.0320.0000.0040.0000.0000.0140.0000.0000.0000.0191.0000.1110.0680.0470.0640.0530.0040.0000.0270.0120.0000.0290.0360.0000.0000.0000.0210.0230.0000.0130.0040.0000.0000.0040.0000.0000.0070.0040.0060.0000.0020.0000.0000.0140.0000.0000.0000.0100.0000.0360.0000.0000.0120.0000.0000.0000.0000.0290.008
Initial_Impact_Type_Rear end0.0020.0060.0120.0100.0210.0000.0000.0200.0070.0120.0870.1111.0000.3070.2130.2860.2400.0150.0100.1380.0210.0060.1320.1570.0660.0260.0460.0720.0500.0180.1020.0800.0260.0000.0120.0480.0150.0310.1090.0160.0110.0340.0000.0090.0420.0160.0000.0040.0000.0080.1570.0010.0080.0250.0090.0710.0000.0000.2050.028
Initial_Impact_Type_SMV other0.0960.1000.0480.0440.0690.0000.0270.0780.0330.0000.0530.0680.3071.0000.1310.1770.1480.0030.0580.1920.0110.0000.0760.2280.1910.0400.0680.0030.0960.0570.3250.0530.0410.0190.0080.0380.0220.0580.0450.0870.0330.0640.0050.0210.0110.0190.0040.0080.0180.0000.2270.0000.0070.0080.0000.0410.0060.0000.1990.015
Initial_Impact_Type_SMV unattended vehicle0.1230.1240.0100.0070.0000.0000.0310.0000.0000.0510.0370.0470.2130.1311.0000.1230.1030.0350.0290.1910.0290.0280.6140.3050.0690.0080.0240.0720.0880.0060.0400.0390.0080.0140.0040.0320.0350.0250.0310.0090.0000.0130.0060.0000.0890.0130.0000.0150.0410.0040.3040.0000.0020.0340.0000.0870.0000.0000.2410.017
Initial_Impact_Type_Sideswipe0.1190.1210.0100.0070.0160.0000.0000.0360.0070.0040.0500.0640.2860.1770.1231.0000.1380.0080.0080.0710.0000.0000.0740.0150.0490.0160.0290.0640.0880.0090.0590.0000.0160.0140.0070.0300.0400.0110.0230.0440.0060.0270.0000.0030.0310.0100.0000.0000.0050.0000.0150.0070.0050.0510.0000.0830.0000.0000.0280.012
Initial_Impact_Type_Turning movement0.0890.0890.0110.0100.0220.0000.0000.0300.0080.0080.0420.0530.2400.1480.1030.1381.0000.0000.0030.0090.0160.0000.0650.1840.0320.0000.0220.0230.0860.0050.0490.0980.0000.0170.0040.0270.0700.0240.0220.0490.0050.0190.0000.0030.0210.0120.0000.0060.0100.0000.1810.0000.0000.0360.0000.0050.0000.0000.1900.005
Lat0.0040.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0150.0030.0350.0080.0001.0000.0000.0020.0000.0000.0290.0060.5970.0000.0000.0000.0020.0000.0000.0000.000-0.015-0.0070.004-0.0230.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0030.0000.0000.0000.000
Light_Dawn0.0040.0030.0080.0290.0110.0000.0060.0470.0000.0040.0170.0000.0100.0580.0290.0080.0030.0001.0000.2230.0340.0000.0300.0270.0280.0020.0000.0040.0000.0000.0000.0130.0050.0020.0000.0000.0000.0000.0160.0500.0000.0370.0000.0000.0140.0150.0000.0000.0180.0000.0260.0000.0000.0000.0020.0110.0000.0000.0170.006
Light_Daylight0.0300.0290.0210.0370.0470.0090.0290.1170.0130.0250.0130.0270.1380.1920.1910.0710.0090.0020.2231.0000.3120.0150.2740.1330.0510.0070.0100.0370.0110.0000.0220.0430.0070.0000.0000.0150.0000.0170.0200.0690.0000.0940.0060.0120.0560.0450.0000.0120.0640.0060.1320.0070.0000.0100.0000.0470.0010.0000.0980.016
Light_Dusk0.0000.0000.0000.0000.0020.0010.0030.0220.0000.0030.0000.0120.0210.0110.0290.0000.0160.0000.0340.3121.0000.0000.0420.0200.0000.0020.0010.0000.0000.0000.0000.0070.0000.0130.0000.0070.0130.0000.0000.0000.0000.0090.0000.0000.0000.0200.0000.0000.0340.0000.0200.0020.0080.0050.0000.0030.0000.0060.0160.000
Light_Other0.0010.0010.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0060.0000.0280.0000.0000.0000.0000.0150.0001.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0080.000
Light_Unknown0.0890.0900.0100.0070.0090.0000.0170.0040.0050.0900.0230.0290.1320.0760.6140.0740.0650.0290.0300.2740.0420.0001.0000.1950.0520.0070.0170.0510.0650.0010.0280.0250.0060.0080.0000.0190.0240.0170.0180.0140.0000.0120.0000.0170.0630.0090.0000.0320.0210.0000.1930.0000.0000.0200.0000.0580.0000.0000.1520.011
Location_Type_Midblock0.0840.0830.0220.0140.0260.0080.0180.0180.0110.0230.0830.0360.1570.2280.3050.0150.1840.0060.0270.1330.0200.0110.1951.0000.1030.0080.0080.0650.0500.0050.0570.0490.0090.0120.0050.0290.0290.0070.0340.0290.0110.0260.0060.0130.0350.0080.0000.0130.0280.0180.9860.0210.0070.1070.0060.3220.0070.0090.7570.070
Long0.0310.0310.0200.0300.0230.0000.0000.0110.0140.0080.0270.0000.0660.1910.0690.0490.0320.5970.0280.0510.0000.0000.0520.1031.0000.0140.0090.0170.0280.0010.0130.0160.0080.0280.0090.0140.0240.0000.0440.0250.0120.0100.0000.0120.0000.0000.0000.0000.0000.0010.1010.0100.0000.0750.0120.0070.0000.0000.0990.010
Max_Injury_Fatal0.0190.0920.0000.0060.0000.0060.0050.0080.0000.0000.0290.0000.0260.0400.0080.0160.0000.0000.0020.0070.0020.0000.0070.0080.0141.0000.0000.0100.0140.0000.0770.0141.0000.0960.0970.0840.0870.0630.0000.0000.0020.0050.0000.0000.0000.0020.0000.0050.0000.0000.0070.0000.0000.0010.0000.0140.0000.0140.0170.000
Max_Injury_Major0.2010.2000.0000.0070.0050.0000.0040.0130.0000.0000.0330.0000.0460.0680.0240.0290.0220.0000.0000.0100.0010.0000.0170.0080.0090.0001.0000.0250.0310.0000.1440.0740.0000.0870.2480.0080.0410.1350.0120.0070.0000.0150.0000.0000.0050.0090.0000.0000.0060.0000.0080.0000.0000.0020.0000.0190.0000.0000.0000.000
Max_Injury_Minimal0.5760.5730.0000.0000.0050.0000.0000.0190.0000.0000.0030.0210.0720.0030.0720.0640.0230.0000.0040.0370.0000.0000.0510.0650.0170.0100.0251.0000.0900.0050.1240.1300.0100.0220.0030.3640.0350.0230.0070.0060.0000.0190.0000.0000.0100.0150.0040.0000.0060.0000.0660.0000.0040.0030.0000.0120.0020.0000.0590.000
Max_Injury_Minor0.7250.7200.0150.0000.0120.0000.0020.0380.0000.0050.0320.0230.0500.0960.0880.0880.0860.0020.0000.0110.0000.0000.0650.0500.0280.0140.0310.0901.0000.0070.2250.1710.0130.0900.0060.0070.3810.1050.0410.0190.0100.0240.0000.0000.0310.0250.0000.0000.0100.0000.0510.0000.0090.0220.0000.0390.0000.0000.0320.003
Max_Injury_None0.0110.0110.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0180.0570.0060.0090.0050.0000.0000.0000.0000.0000.0010.0050.0010.0000.0000.0050.0071.0000.1820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0050.0000.0070.0000.0000.0000.0000.0070.0230.0000.0000.000
Num_Of_Pedestrians0.2940.3010.0020.0000.0080.0000.0180.0230.0000.0000.0170.0130.1020.3250.0400.0590.0490.0000.0000.0220.0000.0000.0280.0570.0130.0770.1440.1240.2250.1821.0000.0070.0520.0190.0110.0260.0570.0000.0040.0190.0000.0180.0000.0000.0120.0130.0000.0000.0310.0000.0580.0170.0280.0110.0000.0180.0320.0000.0490.001
Num_of_Bicycles0.2380.2380.0060.0000.0150.0000.0130.0430.0000.0000.0000.0040.0800.0530.0390.0000.0980.0000.0130.0430.0070.0000.0250.0490.0160.0140.0740.1300.1710.0000.0071.0000.0070.0000.0000.0170.0310.0000.0290.0260.0000.0330.0000.0000.0210.0230.0000.0000.0290.0100.0470.0150.0000.0000.0000.0600.0000.0000.0040.003
Num_of_Fatal_Injuries0.0190.0930.0000.0090.0000.0140.0040.0080.0030.0000.0330.0000.0260.0410.0080.0160.0000.0000.0050.0070.0000.0000.0060.0090.0081.0000.0000.0100.0130.0000.0520.0071.0000.5780.5780.5770.5780.0380.0000.0000.0070.0040.0000.0000.0000.0050.0000.1620.0000.0000.0080.0000.0000.0000.0000.0140.0000.0300.0170.000
Num_of_Injuries0.1070.1120.0000.0000.0000.0000.0000.0090.0000.0000.0250.0000.0000.0190.0140.0140.017-0.0150.0020.0000.0130.0000.0080.0120.0280.0960.0870.0220.0900.0000.0190.0000.5781.0000.2090.6220.7350.0000.0310.0000.0000.0000.0000.0000.0030.0000.0000.1620.0000.0000.0120.0000.0000.0000.0000.0130.0000.0000.0040.000
Num_of_Major_Injuries0.0490.0540.0000.0000.0030.0000.0000.0000.0000.0000.0540.0040.0120.0080.0040.0070.004-0.0070.0000.0000.0000.0000.0000.0050.0090.0970.2480.0030.0060.0000.0110.0000.5780.2091.0000.0090.0420.012-0.0310.0000.0000.0020.0000.0000.0000.0000.0000.1620.0000.0000.0060.0000.0000.0000.0000.0120.0000.0000.0040.000
Num_of_Minimal_Injuries0.2440.2430.0000.0000.0040.0000.0000.0100.0000.0000.0120.0000.0480.0380.0320.0300.0270.0040.0000.0150.0070.0000.0190.0290.0140.0840.0080.3640.0070.0000.0260.0170.5770.6220.0091.0000.0120.0000.0660.0010.0000.0080.0000.0000.0000.0000.0000.1620.0000.0000.0290.0000.0130.0000.0000.0140.0000.0000.0280.000
Num_of_Minor_Injuries0.2880.2880.0000.0000.0000.0000.0060.0150.0170.0000.0540.0000.0150.0220.0350.0400.070-0.0230.0000.0000.0130.0000.0240.0290.0240.0870.0410.0350.3810.0000.0570.0310.5780.7350.0420.0121.0000.0110.0110.0000.0000.0080.0000.0000.0100.0040.0000.1620.0090.0000.0300.0000.0000.0100.0000.0180.0000.0000.0240.000
Num_of_Motorcycles0.1310.1380.0000.0000.0080.0000.0220.0300.0000.0000.0000.0070.0310.0580.0250.0110.0240.0000.0000.0170.0000.0000.0170.0070.0000.0630.1350.0230.1050.0000.0000.0000.0380.0000.0120.0000.0111.0000.0000.0160.0580.0230.0000.0000.0130.0160.3780.0000.0330.0100.0080.0000.0000.0000.0000.0220.0000.0000.0200.000
Num_of_Vehicle0.0410.0400.0070.0000.0010.0000.0060.0150.0000.0130.0000.0040.1090.0450.0310.0230.0220.0710.0160.0200.0000.0000.0180.0340.0440.0000.0120.0070.0410.0000.0040.0290.0000.031-0.0310.0660.0110.0001.0000.0200.0000.0040.0070.0000.0050.0090.0000.0000.0090.0070.0330.0000.0000.0080.0000.0180.0000.0000.0170.000
Road_Surface_Condition_Ice0.0220.0220.0620.0000.3750.0110.0460.0990.0360.0110.0470.0060.0160.0870.0090.0440.0490.0000.0500.0690.0000.0050.0140.0290.0250.0000.0070.0060.0190.0000.0190.0260.0000.0000.0000.0010.0000.0160.0201.0000.0030.0520.0000.0000.0340.0390.0000.0000.0930.0000.0290.0000.0000.0000.0000.0060.0000.0000.0340.000
Road_Surface_Condition_Loose sand or gravel0.0070.0080.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0110.0330.0000.0060.0050.0000.0000.0000.0000.0000.0000.0110.0120.0020.0000.0000.0100.0000.0000.0000.0070.0000.0000.0000.0000.0580.0000.0031.0000.0050.0000.0000.0000.0020.0000.0000.0120.0000.0120.0000.0000.0000.0000.0010.0000.0000.0140.000
Road_Surface_Condition_Loose snow0.0360.0360.0970.0090.0090.0000.0710.5540.0000.0060.0500.0020.0340.0640.0130.0270.0190.0000.0370.0940.0090.0000.0120.0260.0100.0050.0150.0190.0240.0040.0180.0330.0040.0000.0020.0080.0080.0230.0040.0520.0051.0000.0000.0020.0420.0480.0000.0030.1150.0020.0270.0000.0000.0050.0000.0000.0000.0000.0260.000
Road_Surface_Condition_Mud0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0060.0000.0000.0000.0000.0060.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.0000.0000.0000.0000.0000.0010.0000.0060.0000.0000.0000.0000.0000.0000.0000.0050.000
Road_Surface_Condition_Other0.0000.0000.0000.0030.0000.0460.0060.0000.0000.0980.0000.0000.0090.0210.0000.0030.0030.0000.0000.0120.0000.0790.0170.0130.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0001.0000.0000.0000.0000.0000.0080.0000.0130.0000.0000.0000.0000.0000.0000.0000.0110.000
Road_Surface_Condition_Packed snow0.0330.0330.0530.0000.0000.0000.0490.2190.0100.0000.0380.0140.0420.0110.0890.0310.0210.0000.0140.0560.0000.0000.0630.0350.0000.0000.0050.0100.0310.0000.0120.0210.0000.0030.0000.0000.0100.0130.0050.0340.0000.0420.0000.0001.0000.0310.0000.0000.0760.0000.0360.0000.0000.0090.0000.0100.0000.0000.0410.000
Road_Surface_Condition_Slush0.0330.0330.0130.0020.0850.0000.0340.2700.0000.0000.0240.0000.0160.0190.0130.0100.0120.0000.0150.0450.0200.0000.0090.0080.0000.0020.0090.0150.0250.0000.0130.0230.0050.0000.0000.0000.0040.0160.0090.0390.0020.0480.0000.0000.0311.0000.0000.0000.0860.0000.0070.0000.0000.0080.0000.0000.0000.0000.0060.000
Road_Surface_Condition_Spilled liquid0.0070.0070.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.3780.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.008
Road_Surface_Condition_Unknown0.0000.0000.0000.0000.0000.0000.0010.0060.0000.3040.0000.0000.0040.0080.0150.0000.0060.0000.0000.0120.0000.0000.0320.0130.0000.0050.0000.0000.0000.0000.0000.0000.1620.1620.1620.1620.1620.0000.0000.0000.0000.0030.0000.0000.0000.0000.0001.0000.0090.0000.0120.0000.0000.0000.0000.0000.0000.0000.0100.000
Road_Surface_Condition_Wet0.0110.0110.0170.0390.0140.0000.6550.0310.0070.0000.0110.0100.0000.0180.0410.0050.0100.0000.0180.0640.0340.0000.0210.0280.0000.0000.0060.0060.0100.0050.0310.0290.0000.0000.0000.0000.0090.0330.0090.0930.0120.1150.0010.0080.0760.0860.0000.0091.0000.0060.0290.0000.0000.0010.0000.0100.0000.0060.0240.000
Traffic_Control_MPS0.0000.0000.0000.0000.0000.0000.0040.0020.0000.0000.0000.0000.0080.0000.0040.0000.0000.0000.0000.0060.0000.0000.0000.0180.0010.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0100.0070.0000.0000.0020.0000.0000.0000.0000.0000.0000.0061.0000.0190.0000.0000.0000.0000.0050.0000.0000.0160.000
Traffic_Control_No control0.0860.0840.0230.0140.0260.0090.0190.0180.0100.0220.0820.0360.1570.2270.3040.0150.1810.0060.0260.1320.0200.0110.1930.9860.1010.0070.0080.0660.0510.0070.0580.0470.0080.0120.0060.0290.0300.0080.0330.0290.0120.0270.0060.0130.0360.0070.0000.0120.0290.0191.0000.0230.0160.1080.0050.3260.0080.0070.7670.071
Traffic_Control_Other0.0020.0010.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0010.0000.0000.0070.0000.0000.0000.0070.0020.0000.0000.0210.0100.0000.0000.0000.0000.0000.0170.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0231.0000.0000.0000.0000.0070.0000.0000.0200.000
Traffic_Control_Ped. crossover0.0140.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0070.0020.0050.0000.0000.0000.0000.0080.0000.0000.0070.0000.0000.0000.0040.0090.0000.0280.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0001.0000.0000.0000.0040.0000.0000.0140.000
Traffic_Control_Roundabout0.0210.0220.0000.0000.0000.0000.0030.0090.0000.0000.0060.0120.0250.0080.0340.0510.0360.0000.0000.0100.0050.0000.0200.1070.0750.0010.0020.0030.0220.0000.0110.0000.0000.0000.0000.0000.0100.0000.0080.0000.0000.0050.0000.0000.0090.0080.0000.0000.0010.0000.1080.0000.0001.0000.0000.0400.0000.0000.0950.007
Traffic_Control_School bus0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0060.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0001.0000.0000.0000.0000.0040.000
Traffic_Control_Stop sign0.0440.0460.0020.0100.0000.0000.0040.0000.0020.0010.0090.0000.0710.0410.0870.0830.0050.0030.0110.0470.0030.0000.0580.3220.0070.0140.0190.0120.0390.0070.0180.0600.0140.0130.0120.0140.0180.0220.0180.0060.0010.0000.0000.0000.0100.0000.0000.0000.0100.0050.3260.0070.0040.0400.0001.0000.0000.0000.2890.026
Traffic_Control_Traffic controller0.0050.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0010.0000.0000.0000.0070.0000.0000.0000.0020.0000.0230.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0001.0000.0000.0070.000
Traffic_Control_Traffic gate0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0090.0000.0140.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0070.0000.0000.0000.0000.0000.0001.0000.0060.000
Traffic_Control_Traffic signal0.0630.0610.0190.0190.0230.0080.0170.0170.0050.0190.0740.0290.2050.1990.2410.0280.1900.0000.0170.0980.0160.0080.1520.7570.0990.0170.0000.0590.0320.0000.0490.0040.0170.0040.0040.0280.0240.0200.0170.0340.0140.0260.0050.0110.0410.0060.0000.0100.0240.0160.7670.0200.0140.0950.0040.2890.0070.0061.0000.062
Traffic_Control_Yield sign0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0080.0280.0150.0170.0120.0050.0000.0060.0160.0000.0000.0110.0700.0100.0000.0000.0000.0030.0000.0010.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0710.0000.0000.0070.0000.0260.0000.0000.0621.000

Missing values

2024-11-21T00:07:29.044074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-21T00:07:30.710082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Num_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLongAccident_TimestampLocation_Type_MidblockClassification_Of_Accident_Non-fatal injuryClassification_Of_Accident_P.D. onlyInitial_Impact_Type_ApproachingInitial_Impact_Type_OtherInitial_Impact_Type_Rear endInitial_Impact_Type_SMV otherInitial_Impact_Type_SMV unattended vehicleInitial_Impact_Type_SideswipeInitial_Impact_Type_Turning movementRoad_Surface_Condition_IceRoad_Surface_Condition_Loose sand or gravelRoad_Surface_Condition_Loose snowRoad_Surface_Condition_MudRoad_Surface_Condition_OtherRoad_Surface_Condition_Packed snowRoad_Surface_Condition_SlushRoad_Surface_Condition_Spilled liquidRoad_Surface_Condition_UnknownRoad_Surface_Condition_WetEnvironment_Condition_Drifting SnowEnvironment_Condition_Fog, mist, smoke, dustEnvironment_Condition_Freezing RainEnvironment_Condition_OtherEnvironment_Condition_RainEnvironment_Condition_SnowEnvironment_Condition_Strong windEnvironment_Condition_UnknownLight_DawnLight_DaylightLight_DuskLight_OtherLight_UnknownTraffic_Control_MPSTraffic_Control_No controlTraffic_Control_OtherTraffic_Control_Ped. crossoverTraffic_Control_RoundaboutTraffic_Control_School busTraffic_Control_Stop signTraffic_Control_Traffic controllerTraffic_Control_Traffic gateTraffic_Control_Traffic signalTraffic_Control_Yield signMax_Injury_FatalMax_Injury_MajorMax_Injury_MinimalMax_Injury_MinorMax_Injury_None
0100.00.00.00.00.00.00.045.254481-75.9310612017-01-01 01:28:00TrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1200.00.00.00.00.00.00.045.428323-75.6632252017-01-01 03:16:00TrueFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2100.00.00.00.00.00.00.045.324735-75.8267002017-01-01 07:17:00FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
3200.00.00.00.00.00.00.045.338011-75.7259842017-01-01 08:58:00FalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
4200.00.00.00.00.00.00.045.383786-75.6718422017-01-01 11:41:00FalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
5200.00.01.00.01.00.00.045.361850-75.7912322017-01-01 12:08:00FalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
6100.00.00.00.00.00.00.045.452156-75.5944702017-01-01 13:58:00TrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
7200.00.00.00.00.00.00.045.319864-75.7825712017-01-01 20:26:00TrueFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
8100.00.00.00.00.00.00.045.424814-75.6574162017-01-01 20:43:00FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse
9100.00.00.00.00.00.00.045.358637-75.7461902017-01-01 21:40:00FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse
Num_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLongAccident_TimestampLocation_Type_MidblockClassification_Of_Accident_Non-fatal injuryClassification_Of_Accident_P.D. onlyInitial_Impact_Type_ApproachingInitial_Impact_Type_OtherInitial_Impact_Type_Rear endInitial_Impact_Type_SMV otherInitial_Impact_Type_SMV unattended vehicleInitial_Impact_Type_SideswipeInitial_Impact_Type_Turning movementRoad_Surface_Condition_IceRoad_Surface_Condition_Loose sand or gravelRoad_Surface_Condition_Loose snowRoad_Surface_Condition_MudRoad_Surface_Condition_OtherRoad_Surface_Condition_Packed snowRoad_Surface_Condition_SlushRoad_Surface_Condition_Spilled liquidRoad_Surface_Condition_UnknownRoad_Surface_Condition_WetEnvironment_Condition_Drifting SnowEnvironment_Condition_Fog, mist, smoke, dustEnvironment_Condition_Freezing RainEnvironment_Condition_OtherEnvironment_Condition_RainEnvironment_Condition_SnowEnvironment_Condition_Strong windEnvironment_Condition_UnknownLight_DawnLight_DaylightLight_DuskLight_OtherLight_UnknownTraffic_Control_MPSTraffic_Control_No controlTraffic_Control_OtherTraffic_Control_Ped. crossoverTraffic_Control_RoundaboutTraffic_Control_School busTraffic_Control_Stop signTraffic_Control_Traffic controllerTraffic_Control_Traffic gateTraffic_Control_Traffic signalTraffic_Control_Yield signMax_Injury_FatalMax_Injury_MajorMax_Injury_MinimalMax_Injury_MinorMax_Injury_None
74602200.00.00.00.00.00.00.045.353079-75.7834962022-05-31 07:00:00TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74603100.00.00.00.00.00.00.045.291036-76.0122862022-05-31 14:50:00TrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74604100.00.00.00.00.00.00.045.210216-75.7343652022-06-01 09:30:00TrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74605200.00.00.00.00.00.00.045.358515-75.7729192022-06-02 08:28:00TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74606200.00.00.00.00.00.00.045.417398-75.6587752022-06-02 12:38:00TrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74607200.00.00.00.00.00.00.045.362241-76.2336222022-06-03 11:52:00TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74608100.00.00.00.00.00.00.045.338743-75.8411952022-06-03 12:10:00TrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74609500.00.00.00.00.00.00.045.372274-75.7523762022-06-03 14:50:00TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74610300.00.00.00.00.00.00.045.409077-75.6914882022-06-03 14:58:00TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
74611200.00.00.00.00.00.00.045.276966-75.8008632022-06-03 17:05:00TrueFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse

Duplicate rows

Most frequently occurring

Num_of_VehicleNum_Of_PedestriansNum_of_BicyclesNum_of_MotorcyclesNum_of_InjuriesNum_of_Minimal_InjuriesNum_of_Minor_InjuriesNum_of_Major_InjuriesNum_of_Fatal_InjuriesLatLongAccident_TimestampLocation_Type_MidblockClassification_Of_Accident_Non-fatal injuryClassification_Of_Accident_P.D. onlyInitial_Impact_Type_ApproachingInitial_Impact_Type_OtherInitial_Impact_Type_Rear endInitial_Impact_Type_SMV otherInitial_Impact_Type_SMV unattended vehicleInitial_Impact_Type_SideswipeInitial_Impact_Type_Turning movementRoad_Surface_Condition_IceRoad_Surface_Condition_Loose sand or gravelRoad_Surface_Condition_Loose snowRoad_Surface_Condition_MudRoad_Surface_Condition_OtherRoad_Surface_Condition_Packed snowRoad_Surface_Condition_SlushRoad_Surface_Condition_Spilled liquidRoad_Surface_Condition_UnknownRoad_Surface_Condition_WetEnvironment_Condition_Drifting SnowEnvironment_Condition_Fog, mist, smoke, dustEnvironment_Condition_Freezing RainEnvironment_Condition_OtherEnvironment_Condition_RainEnvironment_Condition_SnowEnvironment_Condition_Strong windEnvironment_Condition_UnknownLight_DawnLight_DaylightLight_DuskLight_OtherLight_UnknownTraffic_Control_MPSTraffic_Control_No controlTraffic_Control_OtherTraffic_Control_Ped. crossoverTraffic_Control_RoundaboutTraffic_Control_School busTraffic_Control_Stop signTraffic_Control_Traffic controllerTraffic_Control_Traffic gateTraffic_Control_Traffic signalTraffic_Control_Yield signMax_Injury_FatalMax_Injury_MajorMax_Injury_MinimalMax_Injury_MinorMax_Injury_None# duplicates
0100.00.00.00.00.00.00.045.484600-75.4981032018-11-12 07:08:00FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
1200.00.00.00.00.00.00.045.370006-75.6982012017-09-19 22:38:00FalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
2200.00.00.00.00.00.00.045.373718-75.6843482018-02-21 20:49:00FalseFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2
3200.00.00.00.00.00.00.045.378572-75.6675522019-11-02 19:45:00FalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
4200.00.00.00.00.00.00.045.389919-75.6938592018-09-17 17:50:00FalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
5200.00.00.00.00.00.00.045.438357-75.6777672020-09-19 12:30:00FalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
6200.00.00.00.00.00.00.045.445510-75.4983982022-05-10 12:00:00FalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2
7201.00.01.01.00.00.00.045.421395-75.6988422022-10-03 15:30:00FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalse2